Cluster Identification by a <i>k</i>-means Algorithm-Assisted Imaging Method in a Laboratory-Scale Circulating Fluidized Bed
Bibliographic record
Abstract
Particle clusters for FCC particles in a gas–solid circulating fluidized bed with a 12.4 m high riser and a 5 m high downer were identified from the images of the gas–solid flow by a k-means machine learning algorithm-assisted processing method. An optimal k value of 3 was determined and justified by several evaluation criteria for the k-means algorithm. The solid holdup obtained from the processed images agrees well with that from the optical fiber method. The particle cluster characteristics between the riser and downer, such as the cluster solid holdup, equivalent diameter, velocity, and frequency, were extracted from the processed images and then compared in detail for the first time. The cluster solid holdup and the cluster velocity in the riser (εcl = 0.05–0.20, Vcl = 4–10 m/s) are much higher than those in the downer (εcl = 0.005–0.020, Vcl = 2–5 m/s). The cluster equivalent diameter and the cluster frequency in the riser and downer are similar (dcl = 2–10 mm, fcl = 100–400 Hz). Empirical correlations of the cluster characteristics with the local flow conditions and the operating parameters in both the riser and downer are further studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".